Multi-parametric machine olfaction

ABSTRACT

A system includes an array of chemical, pressure, and temperature sensors, and a temporal airflow modulator configured to provide sniffed vapors in a temporally-modulated sequence through a plurality of different air paths across multiple sensor locations.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/589,668, filed Oct. 1, 2019, which claims benefit from U.S.Provisional Patent Application Ser. No. 62/739,728, filed Oct. 1, 2018,each of which is incorporated by reference in its entirety.

STATEMENT REGARDING GOVERNMENT INTEREST

This invention was made with government support under agreementHR00111720048 awarded by the DARPA Defense Sciences Office. Thegovernment has certain rights in the invention.

BACKGROUND OF THE INVENTION

The present invention relates generally to machine olfaction, and moreparticularly to multi-parametric machine olfaction.

In general, machine olfaction is the automated simulation of the senseof smell. A sense of smell is one of the most fundamental ways thatanimals interact with the world Most animals actively sample ambientodors by sniffing, which introduces chemical samples to the olfactoryreceptors of the nose, and these receptors in turn generate signals thatare decoded by the brain. Many groups have worked towards bio-inspiredmachine olfaction, particularly through the statistical interpretationof a diversity of chemical measurements. However, an important insightinto the biological process is that the brain takes advantage of manytypes of non-chemical information when analyzing odors, includingtemporal, spatial, mechanical, hedonic, and contextual correlations. Incontrast, engineered chemical sensors often ignore this ancillaryinformation. Environmental conditions, when measured, are oftenconsidered only in the context of calibrating chemical measurements.

SUMMARY OF THE INVENTION

The following presents a simplified summary of the innovation in orderto provide a basic understanding of some aspects of the invention. Thissummary is not an extensive overview of the invention. It is intended toneither identify key or critical elements of the invention nor delineatethe scope of the invention. Its sole purpose is to present some conceptsof the invention in a simplified form as a prelude to the more detaileddescription that is presented later.

In general, in one aspect, the invention features a system including anarray of chemical, pressure, and temperature sensors, and a temporalairflow modulator configured to provide sniffed vapors in atemporally-modulated sequence through a plurality of different air pathsacross multiple sensor locations.

In another aspect, the invention features a system including an array ofeight sensor pairs arranged in four rows of two, each pair of sensorsincluding one Volatile Organic Compound (VOC) sensor and one digitalbarometer, a digital-to-analog converter (DAC) whose voltage controlsthe temperature of each of the sensor pairs, a Digital-to-AnalogConverter (DAC), and a Raspberry Pi configured to provide power to thearray eight sensor pairs, and to wirelessly transmits data from thearray of sensor pairs to a host computer configured to analyze the data.

In still another aspect, the invention features a system including apump regulated to a constant airflow by a flow controller, a three-waysolenoid valve configured to selectively pass the airflow and an analytevapor, and a manifold configured to split the airflow containing analytevapor between four small plastic columns containing differentobstructions before reaching a sensing unit.

In yet another aspect, the invention features a method includingproviding an analyte, passing vapor of the analyte through a three-waysolenoid valve configured to selectively pass an airflow and the analytevapor, and passing the analyte vapor through a manifold configured tosplit the airflow containing analyte vapor between four small plasticcolumns containing different obstructions before reaching a sensing unitfor analysis.

These and other features and advantages will be apparent from a readingof the following detailed description and a review of the associateddrawings. It is to be understood that both the foregoing generaldescription and the following detailed description are explanatory onlyand are not restrictive of aspects as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood with reference to the followingdescription, appended claims, and accompanying drawings where:

FIG. 1 is a block diagram of an exemplary system.

FIG. 2 is an exemplary graph.

FIG. 3 is an exemplary test bench.

FIG. 4 is an exemplary comparison.

FIGS. 5(a), 5(b) and 5(c) illustrate representative data for odors.

FIG. 6 is a table.

DETAILED DESCRIPTION

The subject innovation is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the present invention. It may be evident, however, thatthe present invention may be practiced without these specific details.In other instances, well-known structures and devices are shown in blockdiagram form in order to facilitate describing the present invention.

Most implementations of electronic noses (“e-noses”) include an array ofchemical sensors whose outputs are analyzed in parallel at one discretepoint in time. These designs are widely employed across military,industrial, medical, and environmental sciences, with applicationsranging from explosives and disease detection to environmental andindustrial monitoring. Recent advances in compact, portable, andlow-cost sensor designs have been complemented by aggressivemicroelectronic integration.

The present invention is an electronic platform (sometimes referred toherein as “TruffleBot”) which classifies odors using multi-parametricenvironmental information in order to improve upon traditional e-noses.The TruffleBot simultaneously samples pressure, temperature, andchemical time series, while “sniffing” in a temporally modulatedsequence which introduces spatiotemporal time signatures, such astransport delays and diffusive dynamics. These multidimensional signalsdepend on chemical and physical properties which can be unique to aparticular chemical. Additionally, the odor plumes traverse a set offour unique physical pathways which have the aggregate effect ofexpanding the feature space and separability of odors. The system, whichmirrors some of the dynamic contextual features of animal olfaction,improves the performance and accuracy of chemical sensing in a simpleand low-cost hardware platform.

In FIG. 1 , a block diagram of an exemplary system 10 is illustrated.Eight analog metal-oxide gas sensors 12 are digitized while a DAC 14controls their heater voltage while eight digital barometers 16 measurepressure and temperature. The array of eight sensor pairs 12, 16 arearranged in four rows of two, with each position containing one VolatileOrganic Compound (VOC) sensor and one digital barometer. The array ofeight sensor pairs 12, 16 are linked to a Raspberry Pi 18 (85 mm×56 mm).

The VOC sensors 12 (e.g., AMS CCS801) are micro-hotplate metal-oxide(MOX) sensors with integrated resistive heaters. In a MOX gas sensor, ametal oxide film is heated to several hundred degrees Celsius, to atemperature where its electrical conductivity becomes sensitive tochemical interactions with nearby gases. These interactions are complexand non-specific, and MOX sensors will respond to the presence of manydifferent volatile molecules. The heaters of the eight MOX sensors aredriven from the common buffered DAC 14, whose voltage controls thetemperature of the sensors, and in turn, affects their chemicalsensitivity. The MOX resistivity is converted to a voltage and routedthrough a multiplexer 20 into a high precision ADC 22 (e.g., TIADS1256). Components 12, 14, 16, 18, 20 and 22 are referred to hereincollectively as a “TruffleBot.”

In one embodiment, the digital barometers (e.g., ST LPS22HB) are smallMEMS sensors with piezoresistive elements on a thin suspended membrane.These chips measure both temperature and absolute pressure at up to 75samples per second through a serial peripheral interface (SPI) bus.

The TruffleBot is powered entirely through the 5V and 3.3V rails of aRaspberry Pi 18, and consumes approximately 77 mW. The TruffleBot alsohosts several other supporting circuits, including a precision referencegenerator for the MOX sensors, and transistors to switch external 5Vperipherals which may include solenoids and small air pumps. Otherperipherals can also be connected through a Universal Serial Bus (USB)(not shown). Components for one TruffleBot cost approximately $150 US.

The TruffleBot connects to a host computer 24 over Ethernet or WiFi, andmultiple TruffleBots can co-exist on the same network. A host programinitiates an experiment by broadcasting a command for all TruffleBots tobegin data collection. Each TruffleBot saves its sensor traces locally,and when the trial concludes, the host automatically retrieves eachclient's dataset and compiles them all into a single HDFS file foranalysis in MATLAB™ from The Mathworks.

FIG. 2 is an exemplary graph that plots the temperature, pressure andchemical response to a five second exposure to odors from beer (≈6%ethanol). The output of the VOC sensor is expressed as a percentage ofits full scale range, and the pressure and temperature signals deviateonly slightly from ambient. When beer odors are introduced, the pressuredecreases and the temperature increases; both the polarity and magnitudeof these changes depend on the physical properties of the analyte vaporincluding its vapor pressure, density, and molecular weight. Thesedifferences contribute to TruffleBot's overall chemical selectivity.

In FIG. 3 , an exemplary test bench 100 is illustrated in (a). Theoutput of a pump 102 is regulated to a constant flow by a flowcontroller 104, and a three-way solenoid valve 106 (e.g., TakasagoCTV-3) selectively bypasses the analyte vapor 108. The solenoid 106 iscontrolled with a short pseudorandom (PN) binary sequence. The analytesused in these experiments were ambient air (control), apple cidervinegar, lime juice, beer (6.2% ABV), wine (chardonnay, 13% ABV), vodka(40% ABV), ethanol (100% ABV), isopropanol, and acetone. A manifold 109splits the fluid flow between four small plastic columns 110, 112, 114,116 containing different obstructions before reaching the sensor array118. This arrangement allows one to adjust multiple parameters includingthe overall airflow, the solenoid's temporal sequence, the analyte, andthe geometries and contents of the columns.

In (b), an exemplary graph 120 shows differences in the positions andobstructions of four air paths produce different signals in each column,in response to ethanol.

FIG. 4 shows an exemplary comparison of the responses to air, vodka andacetone, at a single array position. The baseline signal levels areaffected by noise and uncontrolled parameters including ambienttemperature, humidity, and atmospheric pressure. The solenoid PNsequence is the same for all trials, and the signal is represented bytemporally correlated changes in the sensor outputs. Assuming a losslesssystem with fixed volumetric flow, the total absolute pressure in eachcolumn head can be represented as

P _(abs) =P _(atm) +P _(ext) +P _(analyte)  (1)

where P_(ext) is the resulting pressure from the constant regulatedairflow and P_(atm) and P_(analyte) are the partial pressures exerted byatmospheric air and the analyte vapor. (When the solenoid bypasses theanalyte, P_(analyte)=0.) According to the Darcy-Weisbach equation,Newtonian fluid flowing through a cylindrical tube experiences apressure drop given by

Δp=σfLv ²/2D  (2)

where p is the fluid density, v is the fluid velocity and f, L, D arethe friction coefficient, length, and diameter of the tube. Since theflow is constant and tube properties do not change, Δp only depends onρ. Thus an analyte with vapor density greater than air would incur morepressure loss in the tube, resulting in a decrease in measured airpressure. For example, P_(abs) decreases during the release of beer(ρ=1.05 g/cm³) but increases for vodka (ρ=0.95 g/cm³).

These pressure changes, in combination with the analyte's physicalproperties (e.g. heat capacity), produce analyte-specific temperaturefluctuations. Using this information, TruffleBot can distinguish betweenanalytes which have similar MOX sensor responsivity, provided thepressure and temperature changes observed are a systematic result of theanalyte's physical properties. For example, in FIG. 4 , vodka andacetone could have been easily discriminated by temperature and pressurealone.

The arrayed sensors and diverse airflow paths support the extraction oftemporal and spatial features. Using the setup in FIG. 3 , the same“sniffing” sequence of 40 pseudorandom bits was applied at 1 bit/secondfor 8 different analytes. Representative data for each odor is shown inFIG. 5(a). The first eight rows represent VOC sensor traces, followed byeight rows of pressure readings and eight rows of temperature readings.The mean value has been subtracted from each trace. The control trialswith ambient air show only small deviations, while VOC magnitudes appearto correlate with alcohol content, as one might anticipate. Some odorsdo not have significant VOC sensor response (lime, vinegar), but do showappreciable pressure and temperature responses.

The experiment was repeated ten times for each analyte, and featurevectors containing the mean, derivative, and standard deviation wereassembled from 0.5 second windows of each of the 24 time series. Weperformed principal-component analysis (PCA) on the combined sensor dataof the nine odor classes (FIG. 5(b)). Even with a comparison of only thefirst two principal components, tightly grouped clusters emerged. Wethen performed 2-fold cross-validation of the results using a simplek-means algorithm over 1000 iterations. The classification approach iscomparable to other e-nose demonstrations, and is one of many possibleclassification strategies (See Table I in FIG. 6 ).

A cross validation accuracy of 90.9% was achieved using only thetransient time series from the MOX sensors, compared to 79.8% if thedata is condensed to only 1 average value per MOX sensor. Addingtemperature and pressure data, error rates reduced by a factor of 2 andaccuracy improved to 95.8%. The confusion matrix in FIG. 5(c) shows thatmost of the errors occurred between lime and vinegar. This can also beseen in their overlapping PCA clusters (FIG. 5(b)), and it is intuitivesince citric acid and acetic acid contain chemically similar functionalgroups. Repeating the classification for the 8 datasets excluding limegives a cross validation accuracy of 98.5%.

It would be appreciated by those skilled in the art that various changesand modifications can be made to the illustrated embodiments withoutdeparting from the spirit of the present invention. All suchmodifications and changes are intended to be within the scope of thepresent invention except as limited by the scope of the appended claims.

1-16. (canceled)
 17. A system comprising: an array of chemical,pressure, and temperature sensors, the array of chemical, pressure, andtemperature sensors comprising an array of sensor pairs; and a temporalairflow modulator configured to provide sniffed vapors in atemporally-modulated sequence through a plurality of different air pathsacross multiple sensor locations.
 18. The system of claim 17 whereineach pair of sensors comprising one Volatile Organic Compound (VOC)sensor and one digital barometer
 19. The system of claim 18 wherein eachVOC sensor comprises a micro-hotplate metal-oxide (MOX) sensor withintegrated resistive heaters configured to respond to a presence ofvolatile molecules.
 20. The system of claim 19 wherein each digitalbarometer comprises a small MEMS sensor with piezoresistive elements ona thin suspended membrane
 21. A system comprising: an array of sensorpairs; a digital-to-analog converter (DAC) whose voltage controls thetemperature of each of the sensor pairs; a Digital-to-Analog Converter(DAC); and a series of single-board computers configured to providepower to the array of eight sensor pairs, and to wirelessly transmitsdata from the array of sensor pairs to a host computer configured toanalyze the data.
 22. The system of claim 21 wherein each pair ofsensors comprising one Volatile Organic Compound (VOC) sensor and onedigital barometer.
 23. The system of claim 22 wherein each VOC sensorcomprises a micro-hotplate metal-oxide (MOX) sensor with integratedresistive heaters configured to respond to a presence of volatilemolecules.
 24. The system of claim 23 wherein each digital barometercomprises a small MEMS sensor with piezoresistive elements on a thinsuspended membrane.